13197 research outputs found
Sort by
Defecation Prediction System Using Bowl Sound
No full textFecal incontinence is a serious but popular problem for elderly people since it not only degrades their quality of physical and mental life but also increases the work of care givers. One promising tool to solve this problem is a defecation prediction system since a patient can go to toilet if he/she knows the time of excretion in advance. Our approach to develop such a system is to measure bowel sounds (BSs) using a wearable device, to predict the defecation time, and to informs the user before defecation. As a first step to the development, in this paper, it is shown that BSs include information of the defecation time by classifying the BSs before/after defecation. The classification is possible by detect the change of the power in the spectrogram of the BSs.technical repor
同一便と同一バス停における乗客数の変動特性を考慮したLSTMによる路線バス乗客数予測
Get PDF代表的な公共交通機関の 1 つとして，路線バスがある．利用者がバスに感じる不満として，車内が混雑していることが挙げられる．また，COVID-19 の防止策として，混雑環境を避けることが利用者に求められている．このような背景からバスの車内混雑状況を予測し，利用者に提供する必要がある．先行研究では，Random Forest を用いて次のバス停出発時の車内混雑度の予測が行われた．しかしながら，特徴量とモデルが十分ではないため十分な精度が出ない結果となっている．そこで本研究では，特徴量を先行研究で使用されていたものだけではなく，N 便前までのバス停出発時の車内人数も用い，モデルを Random Forest から Long Short-Term Memory に変更して車内人数を予測した．先行研究である Arabghalizi らの手法を Random Forest の回帰によって，バス停出発時の車内人数を予測する手法に変更したものと提案手法を，Root Mean Square Error を評価指標として比較した．結果，既存手法よりも提案手法の方が精度が最大で 29% 向上した．conference pape
Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer
No full textBackground: Meta-analyses aggregate results of different clinical studies to assess the effectiveness of a treatment. Despite their importance, meta-analyses are time-consuming and labor-intensive as they involve reading hundreds of research articles and extracting data. The number of research articles is increasing rapidly and most meta-analyses are outdated shortly after publication as new evidence has not been included. Automatic extraction of data from research articles can expedite the meta-analysis process and allow for automatic updates when new results become available. In this study, we propose a system for automatically extracting data from research abstracts and performing statistical analysis.Materials and methods: Our corpus consists of 1011 PubMed abstracts of breast cancer randomized controlled trials annotated with the core elements of clinical trials: Participants, Intervention, Control, and Outcomes (PICO). We proposed a BERT-based named entity recognition (NER) model to identify PICO information from research abstracts. After extracting the PICO information, we parse numeric outcomes to identify the number of patients having certain outcomes for statistical analysis.Results: The NER model extracted PICO elements with relatively high accuracy, achieving F1-scores greater than 0.80 in most entities. We assessed the performance of the proposed system by reproducing the results of an existing meta-analysis. The data extraction step achieved high accuracy, however the statistical analysis step achieved low performance because abstracts sometimes lack all the required information.Conclusion: We proposed a system for automatically extracting data from research abstracts and performing statistical analysis. We evaluated the performance of the system by reproducing an existing meta-analysis and the system achieved a relatively good performance, though more substantiation is required.journal articl
Natural Language Processing: from Bedside to Everywhere
No full textObjectives: Owing to the rapid progress of natural language processing (NLP), the role of NLP in the medical field has radically gained considerable attention from both NLP and medical informatics. Although numerous medical NLP papers are published annually, there is still a gap between basic NLP research and practical product development. This gap raises questions, such as what has medical NLP achieved in each medical field, and what is the burden for the practical use of NLP? This paper aims to clarify the above questions.Methods: We explore the literature on potential NLP products/services applied to various medical/clinical/healthcare areas.Results: This paper introduces clinical applications (bedside applications), in which we introduce the use of NLP for each clinical department, internal medicine, pre-surgery, post-surgery, oncology, radiology, pathology, psychiatry, rehabilitation, obstetrics, and gynecology. Also, we clarify technical problems to be addressed for encouraging bedside applications based on NLP.Conclusions: These results contribute to discussions regarding potentially feasible NLP applications and highlight research gaps for future studies.journal articl
DevReplay: Linter that generates regular expressions for repeating code changes
Get PDFCoding rules automatically exclude language-specific anti-patterns in the source code. However, developers still need to validate project-specific anti-patterns manually. We present a novel static analysis tool DEVREPLAY that generates coding rules as regular expressions from real-time source code editing. The generated regular expressions automatically repeat the same changes to the source code. Hence, users can share their own coding rules with inter-project members to keep source code consistency. As a field study, we submitted patches to the most popular open-source projects implemented by different languages, and project reviewers accepted 80% (8 of 10) of our patches.journal articl
Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire
No full textWe aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross-sectional study, Japanese adults (aged 20?64 years) from Tokyo and surrounding areas were recruited. Participants underwent an oral glucose tolerance test (OGTT) and completed a questionnaire regarding lifestyle and physical characteristics. They were classified into four glycometabolic categories based on the OGTT results: category 1: best glucose metabolism, category 2: low insulin sensitivity, category 3: low insulin secretion, and category 4: combined characteristics of categories 2 and 3. A total of 977 individuals were included; the ratios of participants in categories 1, 2, 3, and 4 were 46%, 21%, 14%, and 19%, respectively. Machine learning models (decision tree, support vector machine, random forest, and XGBoost) were developed for identifying the glycometabolic category using questionnaire responses. Then, the top 10 most important variables in the random forest model were selected, and another random forest model was developed using these variables. Its areas under the receiver operating characteristic curve (AUCs) to classify category 1 and the others, category 2 and the others, category 3 and the others, and category 4 and the others were 0.68 (95% confidence intervals: 0.62?0.75), 0.66 (0.58?0.73), 0.61 (0.51?0.70), and 0.70 (0.62?0.77). For external validation of the model, the same dataset of 452 Japanese adults in Hokkaido was obtained. The AUCs to classify categories 1, 2, 3, and 4 and the others were 0.66 (0.61?0.71), 0.57 (0.51?0.62), 0.60 (0.50?0.69), and 0.64 (0.57?0.71). In conclusion, our model could identify the glucose metabolism status using only 10 factors of lifestyle and physical characteristics. This model may help the larger general population without diabetes to understand their glucose metabolism status and encourage lifestyle improvement to prevent diabetes.journal articl
法律の階層構造を利用した教師あり対照学習による法律検索
No full text本研究では，民法上の法律問題に関する問題文に対して関連する法条を同定する情報検索タスクを扱う．このタスクにおいて近年主流となっている手法は，事前学習済み言語モデルを用いて問題文と各条文との類似度を計算するものである．しかし，これらの手法には，問題文とのn-gram類似度が低い正解条文を検索することが難しいという問題があった． 本研究では，まず，このような難しい正解条文が，n-gram類似度が高い条文と民法の階層構造上で同一クラスにある傾向を示す．この傾向から，同一クラスに位置する条文同士の表現を近づけることができれば，上記の難しい正解条文が検索しやすくなることが考えられる．提案手法では，民法の階層情報を利用した教師あり対照学習を用いてこれを実現する．実験の結果，提案手法を適用することによって，問題文とのn-gram類似度の低い正解条文の検索においてスコアが向上することを確認した．conference pape
負例を厳選した対話応答選択による対話応答生成システムの評価
No full text雑談対話応答生成システムの日々の改良が望ましい方向に効いているか継続的に評価するといった用途として，システムを低コストで評価できる自動評価の枠組みの確立が求められている．しかし，BLEU など，応答生成の自動評価に広く用いられている既存の指標は人間との相関が低いことが報告されている．これは，一つの対話履歴に対し適切な応答が複数存在するという対話の性質に起因する．この性質の影響を受けにくいシステムの評価方法の一つに対話応答選択が考えられる．対話応答選択は，対話履歴に対し適切な応答を応答候補から選ぶタスクである．このタスクではシステムの応答が候補内の発話に限られるため，前述した対話の性質の影響を回避した評価が可能である．一般に対話応答選択では，対話履歴に対する本来の応答（正例）に加え，誤り候補（負例）を無関係な対話データから無作為抽出し応答候補を構成する．しかし，この方法では，正例とかけ離れすぎていて応答として不適切と容易に判別できる発話や，応答として誤りとはいえない発話が負例として候補に混入し，評価の有効性が低下する可能性がある．本論文では，負例を厳選することで不適切な負例の混入を抑制した対話応答選択テストセットの構築方法を提案する．構築したテストセットを用いた対話応答選択によるシステム評価が，BLEU など既存の広く用いられている自動評価指標と比べ人手評価と強く相関することを報告する．journal articl
Prediction of Potential Natural Antibiotics Plants Based on Jamu Formula Using Random Forest Classifier
No full textJamu is the traditional Indonesian herbal medicine system that is considered to have many benefits such as serving as a cure for diseases or maintaining sound health. A Jamu medicine is generally made from a mixture of several herbs. Natural antibiotics can provide a way to handle the problem of antibiotic resistance. This research aims to discover the potential of herbal plants as natural antibiotic candidates based on a machine learning approach. Our input data consists of a list of herbal formulas with plants as their constituents. The target class corresponds to bacterial diseases that can be cured by herbal formulas. The best model has been observed by implementing the Random Forest (RF) algorithm. For 10-fold cross-validations, the maximum accuracy, recall, and precision are 91.10%, 91.10%, and 90.54% with standard deviations 1.05, 1.05, and 1.48, respectively, which imply that the model obtained is good and robust. This study has shown that 14 plants can be potentially used as natural antibiotic candidates. Furthermore, according to scientific journals, 10 of the 14 selected plants have direct or indirect antibacterial activity.journal articl
A Communication Robot for Playing Video Games Together to Boost Motivation for Daily-use
Get PDFNowadays, decreasing opportunities for people to have daily conversations due to the increase in the number of withdrawn young people and households living alone. The lack of daily conversation has been pointed out as a risk that can lead to mental problems such as depression, and serious health problems such as dementia for the elderly. Efforts to encourage daily communication by having communication robots that act as talking partners is attracting attention to solve those problems. One of the challenges of communication robots is the difficulty of maintaining users' motivation to continue using robots. In this study, we propose the communication robot that plays a video game together with a user as an approach to keep the user's motivation high to use the robot. The proposed method not only controls the dialogue content of the robot but also controls the video game situation by manipulating the video game character. In this system, we aim to create an atmosphere where users can enjoy playing video games with the robot together, and the proposed game communication robot can keep their motivation high to use the robot.conference pape